A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

A Greek validation study of the Multiple Sclerosis Work Difficulties Questionnaire-23

The Multiple Sclerosis Work Difficulties Questionnaire-23 (MSWDQ-23) is a self-reportinstrument developed to assess barriers faced by People with Multiple Sclerosis (PwMS) in theworkplace. The aim of this study was to explore the psychometric properties of the Greek versionof the MSWDQ-23. The study sample consisted of 196 PwMS, all currently working in part- orfull-time jobs. Participants underwent clinical examination and cognitive screening with the BriefInternational Cognitive Assessment for Multiple Sclerosis (BICAMS) and completed self-reportmeasures of fatigue, psychological functioning, and quality of life, along with the MSWDQ-23questionnaire. Confirmatory Factor Analysis (CFA) was performed, and goodness-of-fit measureswere used to evaluate construct validity. Convergent validity was checked by correlating MSWDQ-23scores with study measures. Cronbach’s alpha value was produced to assess internal consistency.CFA yielded a model with a fair fit confirming the three-factor structure of the instrument. Higherwork difficulties were associated with higher Expanded Disability Status Scale (EDSS) scores, poorercognitive function, more fatigue, stress, anxiety, and depression, and poorer health status, supportingthe convergent validity of MSWDQ-23. Internal consistency (Cronbach’s alpha = 0.94) and test–retest reliability (ICC = 0.996, 95%, CI = 0.990–0.998) were excellent. The Greek MSWDQ-23 can beconsidered a valid patient-reported outcome measure and can be used in interventions aiming toimprove the vocational status of PwMS

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Segmentation of colour images using variational expectation-maximization

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